3个柔性加工机器来加工3个工件；工件一有三道工序，在三个机器上的加工时间分别为2 4 3；工件二有三道工序，在三个机器上的加工时间分别为5 7 6；工件三有三道工序，在三个机器上的加工时间分别为3 4 2，在Matlab用遗传算法进行编码，；

这个问题可以用遗传算法来求解，以下是MATLAB代码实现：

% 定义工件和机器的数量
num_jobs = 3;
num_machines = 3;

% 定义各工件在各机器上的加工时间
processing_times = [
    2 4 3;
    5 7 6;
    3 4 2;
];

% 定义遗传算法的参数
population_size = 50; % 种群大小
chromosome_length = num_jobs * num_machines; % 染色体长度
generations = 100; % 迭代次数
mutation_rate = 0.05; % 变异率

% 初始化种群
population = randi([0, 1], population_size, chromosome_length);

% 定义适应度函数
fitness_function = @(chromosome) schedule_fitness(chromosome, processing_times, num_jobs, num_machines);

% 迭代进化
for generation = 1:generations
    % 计算种群适应度
    fitness_values = zeros(1, population_size);
    for i = 1:population_size
        fitness_values(i) = fitness_function(population(i, :));
    end
    
    % 选择交叉配对的染色体
    selected_chromosomes = zeros(population_size, chromosome_length);
    for i = 1:population_size
        chromosome1 = population(randi(population_size), :);
        chromosome2 = population(randi(population_size), :);
        if fitness_function(chromosome1) > fitness_function(chromosome2)
            selected_chromosomes(i, :) = chromosome1;
        else
            selected_chromosomes(i, :) = chromosome2;
        end
    end
    
    % 交叉配对
    new_population = zeros(population_size, chromosome_length);
    for i = 1:population_size
        chromosome1 = selected_chromosomes(randi(population_size), :);
        chromosome2 = selected_chromosomes(randi(population_size), :);
        crossover_point = randi(chromosome_length - 1) + 1;
        new_chromosome = [chromosome1(1:crossover_point), chromosome2(crossover_point+1:end)];
        new_population(i, :) = new_chromosome;
    end
    
    % 变异
    for i = 1:population_size
        for j = 1:chromosome_length
            if rand() < mutation_rate
                new_population(i, j) = 1 - new_population(i, j);
            end
        end
    end
    
    % 更新种群
    population = new_population;
end

% 选出最优解
best_chromosome = population(1, :);
best_fitness = fitness_function(best_chromosome);
for i = 2:population_size
    fitness = fitness_function(population(i, :));
    if fitness > best_fitness
        best_chromosome = population(i, :);
        best_fitness = fitness;
    end
end

% 打印最优解和最优解的适应度
disp(best_chromosome);
disp(best_fitness);

% 定义适应度函数
function fitness = schedule_fitness(chromosome, processing_times, num_jobs, num_machines)
    % 解码染色体
    schedule = reshape(chromosome, num_jobs, num_machines);
    
    % 计算每个工件的完成时间
    completion_times = zeros(num_jobs, num_machines);
    for j = 1:num_jobs
        for m = 1:num_machines
            if m == 1
                completion_times(j, m) = processing_times(j, m) * schedule(j, m);
            else
                completion_times(j, m) = completion_times(j, m-1) + processing_times(j, m) * schedule(j, m);
            end
        end
    end
    
    % 计算每个工件的总加工时间
    total_processing_times = max(completion_times, [], 2);
    
    % 计算适应度
    fitness = -sum(total_processing_times);
end

上面的代码中，首先定义了工件和机器的数量，以及各工件在各机器上的加工时间。然后定义了遗传算法的参数，包括种群大小、染色体长度、迭代次数和变异率。接着初始化种群，并定义了适应度函数。在迭代进化中，先计算种群适应度，然后选择交叉配对的染色体，进行交叉配对和变异，最后更新种群。在整个遗传算法的过程中，最重要的是适应度函数，它用于评估染色体的适应度。在这个问题中，适应度函数计算了每个工件的完成时间，然后计算了每个工件的总加工时间，最后返回其相反数作为适应度，这是因为在遗传算法中，适应度越大，表示该染色体的适应度越好。